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by .GlobalEnv when processing object ‘anova_result’
@@ -1861,7 +1796,7 @@ Trust in Science Analysis Notebook
-
+
MinMeanSEMMax <- function(x) {
v <- c(min(x), mean(x) - sd(x)/sqrt(length(x)), mean(x), mean(x) + sd(x)/sqrt(length(x)), max(x))
@@ -1883,7 +1818,7 @@ Trust in Science Analysis Notebook
group_by(complexity, isCovidData) %>%
summarize(n = n(),
mean = mean(vis.trust_6),
- se = sd(data.trust_6)/sqrt(n)),
+ se = sd(vis.trust_6)/sqrt(n)),
aes(x = mean, xend = mean, y = -.25, yend = .25, colour = as.factor(isCovidData)), size = 1) +
# stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 0.5, position = position_nudge(x=0.25, y=0), alpha=0.5) +
@@ -1903,13 +1838,20 @@ Trust in Science Analysis Notebook
legend.position = "none",
axis.text.y = element_blank(),
axis.title.y = element_blank(),
- axis.ticks.y = element_blank())
-
-
-
-ggsave(paste("complexity_dataType_interaction.pdf", sep=""))
-
+ axis.ticks.y = element_blank())`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.ggsave(paste("complexity_dataType_interaction.pdf", sep=""))Saving 7.29 x 4.51 in image
`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.# results$isCovidData <- factor(results$isCovidData, levels = c(0, 1),
# labels = c("Crop Data", "Covid Data"))
@@ -1937,18 +1889,27 @@ Trust in Science Analysis Notebook
geom_jitter(data = results, width = 0.25, height = 0.2, color = "light gray", alpha = 0.5) +
geom_boxplot(lwd = 1, fatten = NULL, width = 0.25, alpha = 0.5, color = "salmon") +
# labs(title = "Trust in data") +
- geom_vline(data = results %>%
+
+
+ geom_segment(data = results %>%
group_by(complexity) %>%
summarize(n = n(),
- data.trust_6 = mean(data.trust_6)),
- aes(xintercept = data.trust_6), size = 1,colour = "salmon") +
+ mean = mean(data.trust_6),
+ se = sd(data.trust_6)/sqrt(n)),
+ aes(x = mean, xend = mean, y = -.25, yend = .25, colour ="salmon"), size = 1) +
+
+
geom_text( data = results %>%
group_by(complexity) %>%
summarize(n = n(),
mean = round(mean(data.trust_6),digits=2),
se = round(sd(data.trust_6)/sqrt(n),digits=2),
- vis.trust_6 = mean(vis.trust_6)),
- aes(label = paste(mean, "[",mean-se,",",mean+se,"]"), x = 6.8, y = 0.43, fontface = 3), size=3, colour = "black")+
+ dadta.trust_6 = mean(data.trust_6)),
+ # aes(label = paste(mean, "[",mean-se,",",mean+se,"]"), x = 6.2, y = 0.43, fontface = 3), size=3, colour = "black")+
+ aes(label = paste(mean), x = mean, y = .35, fontface = 3), size=4, colour = "black")+
+
+
+
facet_grid(rows = vars(complexity)) +
xlab("Trust in Data") +
theme_minimal() +
@@ -1958,33 +1919,285 @@ Trust in Science Analysis Notebook
axis.title.y = element_blank(),
axis.ticks.y = element_blank())
-ggsave(paste("complexity_interaction.pdf", sep=""))
-Saving 7.29 x 4.51 in image
results %>%
+ filter(isCovidData ==0 ) %>%
group_by(complexity) %>%
summarize(n = n(),
- mean = mean(data.trust_6),
- se = sd(data.trust_6)/sqrt(n),
+ mean = mean(vis.trust_6),
+ se = sd(vis.trust_6)/sqrt(n),
n = n)model <- lm(formula = vis.trust_6 ~ complexity * chartType + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results%>%filter(isCovidData == 1)
+ filter(isCovidData ==1 ))Error: unexpected symbol in:
+" data = results%>%filter(isCovidData == 1)
+ filter"model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) + chartType + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results%>% filter(complexity !='moderate'))
+anova(model)Analysis of Variance Table
+
+Response: vis.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 1 3.65 3.652 2.8909 0.090261 .
+as.factor(isCovidData) 1 0.23 0.227 0.1795 0.672144
+chartType 1 1.19 1.189 0.9414 0.332810
+Age 1 5.32 5.317 4.2082 0.041219 *
+Gender 2 6.04 3.022 2.3917 0.093460 .
+State_1 42 95.82 2.282 1.8059 0.003007 **
+Education 8 12.17 1.521 1.2038 0.296826
+Parents_education 2 0.30 0.149 0.1182 0.888523
+Language 3 4.19 1.395 1.1043 0.347861
+Ethnicity 7 9.78 1.396 1.1053 0.359993
+Income 18 25.04 1.391 1.1010 0.351114
+Religion 4 10.79 2.699 2.1361 0.076715 .
+trust.in.science_7 1 77.24 77.236 61.1342 1.291e-13 ***
+need_for_cognition 1 4.73 4.726 3.7406 0.054177 .
+interpersonal.trust_1 1 1.17 1.170 0.9258 0.336843
+complexity:as.factor(isCovidData) 1 0.36 0.362 0.2863 0.593068
+Residuals 263 332.27 1.263
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) + chartType + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results)
+anova(model)Analysis of Variance Table
+
+Response: vis.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 4.32 2.161 1.5759 0.20777
+as.factor(isCovidData) 1 2.48 2.480 1.8087 0.17923
+chartType 1 0.03 0.025 0.0185 0.89188
+trust.in.science_7 1 209.08 209.076 152.4880 < 2e-16 ***
+need_for_cognition 1 8.73 8.729 6.3664 0.01192 *
+interpersonal.trust_1 1 6.28 6.284 4.5831 0.03274 *
+complexity:as.factor(isCovidData) 2 5.26 2.629 1.9177 0.14795
+Residuals 539 739.02 1.371
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Linear Regression Model for trust in vis as a function of
- -model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType
- + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+
+model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType + Age + Gender + State_1 + Income + Education + Parents_education + Language + Ethnicity + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
data = results)
-anova(model)
-
+anova(model)Analysis of Variance Table
+
+Response: vis.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 4.45 2.226 1.7237 0.179624
+as.factor(isCovidData) 1 2.74 2.736 2.1181 0.146282
+chartType 1 0.02 0.020 0.0153 0.901530
+Age 1 9.31 9.308 7.2072 0.007537 **
+Gender 3 7.63 2.544 1.9699 0.117759
+State_1 46 127.87 2.780 2.1523 4.266e-05 ***
+Income 18 36.70 2.039 1.5788 0.061567 .
+Education 9 27.94 3.105 2.4040 0.011443 *
+Parents_education 2 0.91 0.455 0.3526 0.703060
+Language 3 2.25 0.749 0.5797 0.628627
+Ethnicity 8 16.18 2.022 1.5659 0.132820
+Religion 4 14.44 3.610 2.7951 0.025844 *
+trust.in.science_7 1 138.52 138.515 107.2476 < 2.2e-16 ***
+need_for_cognition 1 6.54 6.541 5.0644 0.024919 *
+interpersonal.trust_1 1 6.90 6.900 5.3428 0.021273 *
+complexity:as.factor(isCovidData) 2 3.13 1.564 1.2108 0.298943
+complexity:chartType 2 3.36 1.680 1.3006 0.273429
+as.factor(isCovidData):chartType 1 0.64 0.639 0.4945 0.482305
+complexity:as.factor(isCovidData):chartType 2 0.35 0.175 0.1353 0.873503
+Residuals 437 564.41 1.292
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# can change the predictor to bar.vis
+model<- manova(cbind(vis.trust_6,
+ vis.trust_5,
+ vis.trust_4,
+ vis.trust_3,
+ vis.trust_2,
+ vis.trust_1) ~ complexity * chartType + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results %>%filter(isCovidData == 1))
+summary.aov(model) Response vis.trust_6 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 11.493 5.746 4.6180 0.011151 *
+chartType 1 0.021 0.021 0.0168 0.897075
+Age 1 6.963 6.963 5.5957 0.019138 *
+Gender 3 5.465 1.822 1.4639 0.226227
+State_1 44 138.867 3.156 2.5364 1.075e-05 ***
+Education 9 27.573 3.064 2.4621 0.011600 *
+Parents_education 2 7.254 3.627 2.9150 0.056936 .
+Language 3 5.740 1.913 1.5376 0.206617
+Ethnicity 8 29.014 3.627 2.9146 0.004517 **
+Income 18 39.852 2.214 1.7792 0.031312 *
+Religion 4 27.389 6.847 5.5028 0.000345 ***
+trust.in.science_7 1 94.704 94.704 76.1086 2.463e-15 ***
+need_for_cognition 1 0.249 0.249 0.2004 0.654956
+interpersonal.trust_1 1 7.605 7.605 6.1114 0.014421 *
+complexity:chartType 2 1.003 0.502 0.4032 0.668814
+Residuals 169 210.292 1.244
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_5 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 30.29 15.146 5.1029 0.0070499 **
+chartType 1 2.06 2.057 0.6931 0.4062850
+Age 1 6.87 6.872 2.3153 0.1299766
+Gender 3 3.30 1.100 0.3706 0.7742962
+State_1 44 122.01 2.773 0.9342 0.5927361
+Education 9 30.20 3.356 1.1305 0.3438199
+Parents_education 2 21.91 10.955 3.6908 0.0269858 *
+Language 3 4.89 1.631 0.5494 0.6491861
+Ethnicity 8 26.48 3.310 1.1150 0.3554987
+Income 18 75.86 4.214 1.4198 0.1276477
+Religion 4 15.18 3.794 1.2783 0.2805024
+trust.in.science_7 1 40.05 40.055 13.4946 0.0003208 ***
+need_for_cognition 1 9.52 9.516 3.2059 0.0751611 .
+interpersonal.trust_1 1 0.86 0.862 0.2904 0.5906510
+complexity:chartType 2 3.42 1.709 0.5757 0.5634154
+Residuals 169 501.62 2.968
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_4 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 18.79 9.393 2.8811 0.05883 .
+chartType 1 0.01 0.008 0.0026 0.95962
+Age 1 1.23 1.230 0.3774 0.53982
+Gender 3 3.55 1.183 0.3629 0.77986
+State_1 44 144.29 3.279 1.0058 0.47167
+Education 9 36.19 4.021 1.2333 0.27773
+Parents_education 2 14.48 7.242 2.2211 0.11165
+Language 3 1.42 0.473 0.1452 0.93266
+Ethnicity 8 40.47 5.059 1.5516 0.14299
+Income 18 66.24 3.680 1.1288 0.32848
+Religion 4 25.68 6.421 1.9695 0.10138
+trust.in.science_7 1 73.52 73.520 22.5499 4.347e-06 ***
+need_for_cognition 1 17.75 17.748 5.4435 0.02082 *
+interpersonal.trust_1 1 0.54 0.542 0.1664 0.68386
+complexity:chartType 2 5.59 2.793 0.8567 0.42640
+Residuals 169 550.99 3.260
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_3 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 19.559 9.7794 5.2355 0.0062214 **
+chartType 1 1.574 1.5745 0.8429 0.3598694
+Age 1 2.247 2.2467 1.2028 0.2743263
+Gender 3 12.010 4.0033 2.1432 0.0966560 .
+State_1 44 118.874 2.7017 1.4464 0.0506440 .
+Education 9 24.871 2.7635 1.4795 0.1590611
+Parents_education 2 14.778 7.3889 3.9558 0.0209426 *
+Language 3 0.228 0.0759 0.0406 0.9890404
+Ethnicity 8 9.536 1.1920 0.6381 0.7448722
+Income 18 33.244 1.8469 0.9888 0.4750210
+Religion 4 14.803 3.7006 1.9812 0.0995774 .
+trust.in.science_7 1 26.452 26.4515 14.1612 0.0002312 ***
+need_for_cognition 1 3.476 3.4764 1.8611 0.1743091
+interpersonal.trust_1 1 4.961 4.9615 2.6562 0.1050099
+complexity:chartType 2 1.600 0.8000 0.4283 0.6523172
+Residuals 169 315.673 1.8679
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_2 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 38.917 19.4584 13.2888 4.365e-06 ***
+chartType 1 4.965 4.9649 3.3907 0.06732 .
+Age 1 0.610 0.6097 0.4164 0.51962
+Gender 3 13.188 4.3961 3.0023 0.03206 *
+State_1 44 105.817 2.4049 1.6424 0.01352 *
+Education 9 7.175 0.7972 0.5444 0.84042
+Parents_education 2 7.305 3.6523 2.4943 0.08559 .
+Language 3 0.245 0.0816 0.0557 0.98264
+Ethnicity 8 14.155 1.7693 1.2083 0.29684
+Income 18 33.283 1.8491 1.2628 0.21827
+Religion 4 9.240 2.3100 1.5776 0.18250
+trust.in.science_7 1 5.291 5.2911 3.6135 0.05902 .
+need_for_cognition 1 3.732 3.7323 2.5489 0.11224
+interpersonal.trust_1 1 1.851 1.8509 1.2640 0.26248
+complexity:chartType 2 0.428 0.2141 0.1462 0.86406
+Residuals 169 247.462 1.4643
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_1 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 5.55 2.774 1.4342 0.24120
+chartType 1 0.00 0.003 0.0013 0.97098
+Age 1 7.35 7.349 3.7990 0.05294 .
+Gender 3 6.91 2.302 1.1901 0.31516
+State_1 44 126.21 2.868 1.4828 0.04008 *
+Education 9 7.14 0.794 0.4104 0.92833
+Parents_education 2 4.62 2.312 1.1952 0.30519
+Language 3 2.86 0.953 0.4929 0.68769
+Ethnicity 8 16.68 2.085 1.0779 0.38093
+Income 18 34.76 1.931 0.9982 0.46434
+Religion 4 12.66 3.166 1.6366 0.16730
+trust.in.science_7 1 39.14 39.145 20.2362 1.268e-05 ***
+need_for_cognition 1 2.43 2.435 1.2586 0.26350
+interpersonal.trust_1 1 10.48 10.477 5.4160 0.02114 *
+complexity:chartType 2 1.76 0.878 0.4541 0.63577
+Residuals 169 326.91 1.934
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+2 observations deleted due to missingnesscolinearity_model <- lm(formula = trust.in.science_7 ~ affect.aesthetic_1 + affect.clarity_1 + affect.science_1 + vis.trust_1 + vis.trust_2 + vis.trust_3 + vis.trust_4 + vis.trust_5 + vis.trust_6 + data.trust_6 + data.trust_5 + data.trust_4 + data.trust_3 + data.trust_2 + data.trust_1,
+
+colinearity_model <- lm(formula = Age ~ vis.trust_1 + vis.trust_2 + vis.trust_3 + affect.science_1 + affect.clarity_1 + affect.aesthetic_1 + vis.trust_6 + data.trust_1 + data.trust_2 + data.trust_3 + data.trust_4 + data.trust_5 + data.trust_6 + interpersonal.trust_1 + trust.in.science_7 + need_for_cognition,
data = results)
vif(colinearity_model)
+
+ vis.trust_1 vis.trust_2 vis.trust_3 affect.science_1 affect.clarity_1 affect.aesthetic_1 vis.trust_6 data.trust_1 data.trust_2
+ 2.203331 3.076587 2.611914 1.417935 1.562866 1.263335 3.161843 3.324068 2.300680
+ data.trust_3 data.trust_4 data.trust_5 data.trust_6 interpersonal.trust_1 trust.in.science_7 need_for_cognition
+ 2.229731 2.125802 1.689881 4.388363 1.149877 1.422811 1.117929
+
vif(colinearity_model)relation of trust in vis and trust in data
-
-data_frame = data.frame(results$vis.trust_1, results$vis.trust_2, results$vis.trust_3, results$vis.trust_4, results$vis.trust_5, results$vis.trust_6, results$data.trust_1, results$data.trust_2, results$data.trust_3, results$data.trust_4, results$data.trust_5, results$data.trust_6)
+
+data_frame = data.frame(results$vis.trust_1, results$vis.trust_2, results$vis.trust_3, results$affect.science_1, results$affect.clarity_1, results$affect.aesthetic_1, results$vis.trust_6, results$data.trust_1, results$data.trust_2, results$data.trust_3, results$data.trust_4, results$data.trust_5, results$data.trust_6, results$interpersonal.trust_1, results$trust.in.science_7, results$need_for_cognition)
cor(data_frame)
@@ -2068,13 +2287,36 @@ Trust in science, need for cognition, and interpersonal trust on
-
-model <- lm(formula = data.trust_6 ~ complexity * as.factor(isCovidData) * chartType
+
+model <- lm(formula = data.trust_6 ~ complexity * chartType
+ Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
- data = results)
-anova(model)
-
+ data = results%>% filter(isCovidData == 1))
+anova(model)
+
+Analysis of Variance Table
+
+Response: data.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 3.497 1.749 1.3088 0.272876
+chartType 1 0.889 0.889 0.6653 0.415858
+Age 1 13.358 13.358 9.9976 0.001858 **
+Gender 3 6.582 2.194 1.6421 0.181552
+State_1 44 187.576 4.263 3.1907 4.012e-08 ***
+Education 9 26.902 2.989 2.2372 0.021915 *
+Parents_education 2 9.807 4.903 3.6700 0.027529 *
+Language 3 1.136 0.379 0.2835 0.837286
+Ethnicity 8 19.808 2.476 1.8532 0.070531 .
+Income 18 52.288 2.905 2.1742 0.005527 **
+Religion 4 25.772 6.443 4.8222 0.001044 **
+trust.in.science_7 1 153.507 153.507 114.8923 < 2.2e-16 ***
+need_for_cognition 1 0.377 0.377 0.2823 0.595918
+interpersonal.trust_1 1 8.065 8.065 6.0361 0.015026 *
+complexity:chartType 2 0.103 0.051 0.0384 0.962300
+Residuals 169 225.800 1.336
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
@@ -2206,6 +2448,137 @@ Do the trust items predict trust?
data = results)
summary(model)
+
+
+Call:
+lm(formula = vis.trust_6 ~ vis.trust_1 + vis.trust_2 + vis.trust_3 +
+ vis.trust_4 + vis.trust_5 + affect.science_1 + affect.clarity_1 +
+ affect.aesthetic_1 + Age + Gender + State_1 + Education +
+ Parents_education + Language + Ethnicity + Income + Religion +
+ trust.in.science_7 + need_for_cognition + interpersonal.trust_1,
+ data = results)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-2.83565 -0.50664 0.01138 0.51001 2.58176
+
+Coefficients: (1 not defined because of singularities)
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) -11.568583 5.984157 -1.933 0.053853 .
+vis.trust_1 0.215786 0.038071 5.668 2.62e-08 ***
+vis.trust_2 0.150292 0.047905 3.137 0.001820 **
+vis.trust_3 0.188862 0.048434 3.899 0.000111 ***
+vis.trust_4 0.056337 0.034117 1.651 0.099392 .
+vis.trust_5 0.020632 0.033502 0.616 0.538318
+affect.science_1 0.006861 0.002558 2.682 0.007601 **
+affect.clarity_1 -0.000636 0.001958 -0.325 0.745437
+affect.aesthetic_1 -0.001043 0.002041 -0.511 0.609684
+Age 0.007716 0.002930 2.633 0.008758 **
+Gender2 0.086332 0.085376 1.011 0.312475
+Gender3 -0.545730 0.405884 -1.345 0.179464
+Gender5 -1.519986 1.377488 -1.103 0.270436
+State_1Alaska 1.322914 1.734212 0.763 0.445972
+State_1Arizona 0.121891 0.383528 0.318 0.750776
+State_1Arkansas -0.031368 0.531276 -0.059 0.952944
+State_1California -0.052693 0.268334 -0.196 0.844409
+State_1Colorado -0.162685 0.376532 -0.432 0.665908
+State_1Connecticut -0.319555 0.477459 -0.669 0.503665
+State_1Delaware 0.335401 0.691998 0.485 0.628141
+State_1Florida -0.267266 0.287427 -0.930 0.352956
+State_1Georgia -0.212548 0.304372 -0.698 0.485348
+State_1Hawaii -0.274540 0.601487 -0.456 0.648302
+State_1Illinois -0.392732 0.321003 -1.223 0.221813
+State_1Indiana -0.359962 0.397884 -0.905 0.366124
+State_1Iowa -0.343904 0.532551 -0.646 0.518767
+State_1Kansas -0.112709 0.421055 -0.268 0.789069
+State_1Kentucky -0.693997 0.390328 -1.778 0.076097 .
+State_1Louisiana -0.551437 0.445468 -1.238 0.216420
+State_1Maine 0.801057 0.583361 1.373 0.170397
+State_1Maryland -0.143161 0.344298 -0.416 0.677755
+State_1Massachusetts -0.111028 0.351510 -0.316 0.752258
+State_1Michigan -0.313457 0.330148 -0.949 0.342916
+State_1Minnesota -0.376869 0.476941 -0.790 0.429848
+State_1Mississippi -1.239929 0.519474 -2.387 0.017413 *
+State_1Missouri -0.818462 0.389421 -2.102 0.036144 *
+State_1Montana 0.049043 0.707315 0.069 0.944754
+State_1Nebraska -0.047164 0.585113 -0.081 0.935791
+State_1Nevada -1.201963 0.507768 -2.367 0.018358 *
+State_1New Hampshire -1.584564 0.947064 -1.673 0.095012 .
+State_1New Jersey -0.114745 0.409673 -0.280 0.779541
+State_1New York 0.100077 0.297830 0.336 0.737016
+State_1North Carolina -0.237599 0.317988 -0.747 0.455345
+State_1Ohio -0.205485 0.338339 -0.607 0.543941
+State_1Oklahoma 0.042281 0.455457 0.093 0.926079
+State_1Oregon -0.426634 0.410003 -1.041 0.298650
+State_1Pennsylvania -0.233936 0.296140 -0.790 0.429981
+State_1Rhode Island -0.488928 0.590033 -0.829 0.407755
+State_1South Carolina -0.507633 0.477715 -1.063 0.288534
+State_1South Dakota -2.119380 0.964038 -2.198 0.028438 *
+State_1Tennessee -0.407836 0.361310 -1.129 0.259610
+State_1Texas -0.391497 0.286211 -1.368 0.172054
+State_1Utah -0.430443 0.446138 -0.965 0.335165
+State_1Vermont -1.340000 0.693503 -1.932 0.053974 .
+State_1Virginia -0.404289 0.406598 -0.994 0.320614
+State_1Washington 0.158682 0.321748 0.493 0.622127
+State_1West Virginia -0.452496 0.578523 -0.782 0.434542
+State_1Wisconsin -0.183009 0.327284 -0.559 0.576327
+State_1Wyoming -0.378580 0.959063 -0.395 0.693226
+Education2 -0.293617 0.395770 -0.742 0.458550
+Education3 -0.477211 0.384007 -1.243 0.214635
+Education4 -0.569974 0.410837 -1.387 0.166038
+Education5 -0.424505 0.415909 -1.021 0.307972
+Education6 -0.662040 0.377600 -1.753 0.080249 .
+Education7 -0.413820 0.390888 -1.059 0.290333
+Education8 -0.174992 0.464790 -0.376 0.706729
+Education9 -0.047943 0.632478 -0.076 0.939612
+Education10 -3.043541 0.998056 -3.049 0.002431 **
+Parents_education2 0.030904 0.104267 0.296 0.767069
+Parents_education3 -0.012182 0.117568 -0.104 0.917520
+Language2 -0.105108 0.529395 -0.199 0.842711
+Language3 0.334894 0.512609 0.653 0.513896
+Language4 -0.865349 0.857233 -1.009 0.313305
+Ethnicity2 -1.889884 1.029641 -1.835 0.067110 .
+Ethnicity3 -1.646928 1.012601 -1.626 0.104574
+Ethnicity4 -1.638000 1.045092 -1.567 0.117757
+Ethnicity5 -0.808707 1.195565 -0.676 0.499128
+Ethnicity6 -0.345946 1.369488 -0.253 0.800687
+Ethnicity7 -1.825416 1.010992 -1.806 0.071669 .
+Ethnicity8 -1.621890 1.019411 -1.591 0.112326
+Ethnicity9 -1.733401 1.014518 -1.709 0.088231 .
+Ethnicity10 NA NA NA NA
+Income2 -0.863563 0.425063 -2.032 0.042793 *
+Income3 -0.847871 0.350137 -2.422 0.015858 *
+Income4 -0.979911 0.333680 -2.937 0.003492 **
+Income5 -0.777323 0.336630 -2.309 0.021399 *
+Income6 -0.930965 0.335217 -2.777 0.005717 **
+Income7 -0.834286 0.338312 -2.466 0.014042 *
+Income8 -0.571733 0.355942 -1.606 0.108936
+Income9 -0.913670 0.353118 -2.587 0.009989 **
+Income10 -0.594093 0.357703 -1.661 0.097454 .
+Income11 -1.063443 0.355617 -2.990 0.002942 **
+Income12 -0.929792 0.367972 -2.527 0.011860 *
+Income13 -0.950678 0.377698 -2.517 0.012189 *
+Income14 -1.177312 0.352011 -3.345 0.000895 ***
+Income15 -0.589763 0.474481 -1.243 0.214543
+Income16 -1.139667 0.369668 -3.083 0.002179 **
+Income17 -0.838200 0.340184 -2.464 0.014122 *
+Income18 -1.494424 0.721342 -2.072 0.038873 *
+Income19 -1.057261 0.400257 -2.641 0.008549 **
+Religion2 -0.118556 0.189118 -0.627 0.531056
+Religion3 -0.197893 0.186632 -1.060 0.289570
+Religion4 -0.036543 0.180259 -0.203 0.839444
+Religion5 0.032519 0.404114 0.080 0.935901
+trust.in.science_7 0.161617 0.022364 7.227 2.20e-12 ***
+need_for_cognition -0.046815 0.071523 -0.655 0.513109
+interpersonal.trust_1 0.055705 0.033848 1.646 0.100526
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 0.8806 on 440 degrees of freedom
+ (3 observations deleted due to missingness)
+Multiple R-squared: 0.6498, Adjusted R-squared: 0.5662
+F-statistic: 7.776 on 105 and 440 DF, p-value: < 2.2e-16
+
@@ -2222,6 +2595,133 @@ Do the trust items predict trust?
data = results)
summary(model)
+Call:
+lm(formula = data.trust_6 ~ data.trust_1 + data.trust_2 + data.trust_3 +
+ data.trust_4 + data.trust_5 + Age + Gender + State_1 + Education +
+ Parents_education + Language + Ethnicity + Income + Religion +
+ trust.in.science_7 + need_for_cognition + interpersonal.trust_1,
+ data = results)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-2.2352 -0.3538 0.0000 0.4104 2.2492
+
+Coefficients: (1 not defined because of singularities)
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) -1.316e+01 5.014e+00 -2.623 0.009005 **
+data.trust_1 5.598e-01 4.243e-02 13.194 < 2e-16 ***
+data.trust_2 1.124e-01 3.145e-02 3.573 0.000392 ***
+data.trust_3 1.875e-01 3.387e-02 5.535 5.33e-08 ***
+data.trust_4 1.912e-02 3.519e-02 0.543 0.587117
+data.trust_5 6.567e-02 3.099e-02 2.119 0.034662 *
+Age 6.111e-03 2.460e-03 2.485 0.013339 *
+Gender2 -3.429e-02 7.182e-02 -0.477 0.633319
+Gender3 -6.200e-01 3.463e-01 -1.791 0.074037 .
+Gender5 2.591e-01 1.164e+00 0.222 0.824036
+State_1Alaska 2.958e-01 1.446e+00 0.205 0.838027
+State_1Arizona -2.353e-01 3.235e-01 -0.727 0.467429
+State_1Arkansas 3.650e-02 4.519e-01 0.081 0.935655
+State_1California -2.765e-01 2.268e-01 -1.219 0.223556
+State_1Colorado -1.135e-01 3.185e-01 -0.357 0.721633
+State_1Connecticut -2.469e-01 4.076e-01 -0.606 0.545041
+State_1Delaware -3.506e-02 5.847e-01 -0.060 0.952203
+State_1Florida 3.851e-03 2.433e-01 0.016 0.987376
+State_1Georgia -2.070e-02 2.573e-01 -0.080 0.935920
+State_1Hawaii -1.198e-01 5.066e-01 -0.236 0.813221
+State_1Illinois -2.293e-01 2.744e-01 -0.835 0.403907
+State_1Indiana -9.702e-01 3.353e-01 -2.894 0.003993 **
+State_1Iowa -2.097e-01 4.502e-01 -0.466 0.641602
+State_1Kansas -1.939e-01 3.543e-01 -0.547 0.584541
+State_1Kentucky -2.129e-01 3.305e-01 -0.644 0.519800
+State_1Louisiana -4.327e-01 3.758e-01 -1.151 0.250216
+State_1Maine -9.343e-04 4.892e-01 -0.002 0.998477
+State_1Maryland 6.313e-02 2.910e-01 0.217 0.828371
+State_1Massachusetts -1.000e-02 2.966e-01 -0.034 0.973111
+State_1Michigan -1.170e-01 2.784e-01 -0.420 0.674586
+State_1Minnesota -1.259e-02 4.022e-01 -0.031 0.975048
+State_1Mississippi -8.418e-01 4.401e-01 -1.913 0.056430 .
+State_1Missouri -2.860e-01 3.271e-01 -0.874 0.382468
+State_1Montana -3.628e-01 5.914e-01 -0.613 0.539903
+State_1Nebraska -1.390e-02 4.931e-01 -0.028 0.977530
+State_1Nevada -5.972e-02 4.258e-01 -0.140 0.888509
+State_1New Hampshire 8.429e-02 7.988e-01 0.106 0.916012
+State_1New Jersey -4.443e-01 3.448e-01 -1.289 0.198243
+State_1New York -2.169e-02 2.528e-01 -0.086 0.931673
+State_1North Carolina 3.833e-02 2.667e-01 0.144 0.885784
+State_1Ohio -3.252e-02 2.841e-01 -0.114 0.908943
+State_1Oklahoma 3.489e-01 3.843e-01 0.908 0.364442
+State_1Oregon 1.630e-01 3.457e-01 0.471 0.637568
+State_1Pennsylvania -1.478e-01 2.489e-01 -0.594 0.552877
+State_1Rhode Island -4.683e-01 4.941e-01 -0.948 0.343804
+State_1South Carolina -2.846e-01 4.037e-01 -0.705 0.481187
+State_1South Dakota -2.177e+00 8.041e-01 -2.707 0.007051 **
+State_1Tennessee -5.759e-01 3.043e-01 -1.893 0.059033 .
+State_1Texas -2.713e-01 2.417e-01 -1.123 0.262213
+State_1Utah -1.505e-01 3.770e-01 -0.399 0.689941
+State_1Vermont 3.256e-02 5.842e-01 0.056 0.955578
+State_1Virginia 1.386e-01 3.438e-01 0.403 0.686999
+State_1Washington -1.624e-01 2.694e-01 -0.603 0.546903
+State_1West Virginia -2.660e-01 4.864e-01 -0.547 0.584694
+State_1Wisconsin 1.575e-02 2.755e-01 0.057 0.954439
+State_1Wyoming -8.592e-02 8.082e-01 -0.106 0.915383
+Education2 1.740e-01 3.308e-01 0.526 0.599145
+Education3 1.638e-01 3.213e-01 0.510 0.610480
+Education4 3.578e-01 3.436e-01 1.041 0.298215
+Education5 4.338e-01 3.504e-01 1.238 0.216359
+Education6 1.346e-01 3.159e-01 0.426 0.670237
+Education7 1.471e-01 3.283e-01 0.448 0.654383
+Education8 6.090e-01 3.919e-01 1.554 0.120852
+Education9 -8.566e-01 5.304e-01 -1.615 0.107015
+Education10 7.685e-02 8.248e-01 0.093 0.925809
+Parents_education2 3.662e-02 8.801e-02 0.416 0.677566
+Parents_education3 4.423e-03 9.821e-02 0.045 0.964097
+Language2 3.625e-01 4.464e-01 0.812 0.417253
+Language3 2.926e-01 4.337e-01 0.675 0.500217
+Language4 5.497e-01 7.222e-01 0.761 0.446967
+Ethnicity2 -1.413e-01 8.710e-01 -0.162 0.871231
+Ethnicity3 -6.553e-02 8.571e-01 -0.076 0.939095
+Ethnicity4 -1.685e-01 8.846e-01 -0.190 0.849045
+Ethnicity5 -3.473e-01 1.008e+00 -0.345 0.730541
+Ethnicity6 1.352e+00 1.149e+00 1.176 0.240098
+Ethnicity7 -1.305e-01 8.561e-01 -0.152 0.878884
+Ethnicity8 -4.033e-01 8.620e-01 -0.468 0.640066
+Ethnicity9 -6.418e-02 8.591e-01 -0.075 0.940487
+Ethnicity10 NA NA NA NA
+Income2 1.390e-01 3.571e-01 0.389 0.697266
+Income3 1.915e-01 2.922e-01 0.655 0.512637
+Income4 2.520e-01 2.806e-01 0.898 0.369530
+Income5 2.478e-01 2.819e-01 0.879 0.379910
+Income6 2.070e-01 2.816e-01 0.735 0.462661
+Income7 3.353e-01 2.826e-01 1.186 0.236205
+Income8 3.153e-01 2.976e-01 1.059 0.290062
+Income9 3.141e-01 2.950e-01 1.065 0.287521
+Income10 4.680e-01 2.997e-01 1.562 0.119034
+Income11 3.063e-01 2.966e-01 1.033 0.302300
+Income12 2.137e-01 3.097e-01 0.690 0.490494
+Income13 1.122e-01 3.176e-01 0.353 0.723933
+Income14 2.085e-01 2.960e-01 0.704 0.481544
+Income15 5.904e-01 3.992e-01 1.479 0.139880
+Income16 -9.696e-04 3.092e-01 -0.003 0.997499
+Income17 4.046e-01 2.846e-01 1.421 0.155949
+Income18 4.514e-01 6.083e-01 0.742 0.458455
+Income19 -8.688e-02 3.360e-01 -0.259 0.796119
+Religion2 9.832e-02 1.591e-01 0.618 0.536837
+Religion3 8.967e-02 1.567e-01 0.572 0.567443
+Religion4 9.837e-02 1.521e-01 0.647 0.518024
+Religion5 6.231e-01 3.376e-01 1.845 0.065640 .
+trust.in.science_7 6.755e-02 1.965e-02 3.438 0.000642 ***
+need_for_cognition 1.162e-01 5.983e-02 1.942 0.052722 .
+interpersonal.trust_1 4.233e-02 2.839e-02 1.491 0.136678
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 0.7431 on 443 degrees of freedom
+ (3 observations deleted due to missingness)
+Multiple R-squared: 0.7791, Adjusted R-squared: 0.7282
+F-statistic: 15.32 on 102 and 443 DF, p-value: < 2.2e-16How does performance on VLAT questions predict trust?
- -model <- lm(formula = vis.trust_6 ~ vlat_simple * vlat_moderate * vlat_complex +
+
+model <- lm(formula = vis.trust_6 ~ assigned_vlat *
Age + Gender + State_1 + Education + Parents_education + Language +
Ethnicity + Income + Religion + trust.in.science_7 +
need_for_cognition + interpersonal.trust_1,
@@ -2703,16 +3203,54 @@ Affect on Trust {it’s own section}
Trust in Data
-
+
model <- lm(formula = data.trust_6 ~ affect.science_1 * affect.clarity_1 * affect.aesthetic_1 +
Age + Gender + State_1 + Education + Parents_education + Language +
Ethnicity + Income + Religion + trust.in.science_7 +
need_for_cognition + interpersonal.trust_1,
data = results)
-anova(model)
-
-emmeans(aov(data.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)
+anova(model)
+
+
+Analysis of Variance Table
+
+Response: data.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+affect.science_1 1 215.22 215.219 177.0086 < 2.2e-16 ***
+affect.clarity_1 1 9.25 9.255 7.6117 0.0060405 **
+affect.aesthetic_1 1 7.35 7.346 6.0415 0.0143574 *
+Age 1 11.87 11.874 9.7662 0.0018948 **
+Gender 3 12.52 4.175 3.4337 0.0169859 *
+State_1 46 102.32 2.224 1.8295 0.0011628 **
+Education 9 23.48 2.609 2.1460 0.0247518 *
+Parents_education 2 2.76 1.378 1.1336 0.3228214
+Language 3 3.61 1.204 0.9902 0.3972282
+Ethnicity 8 9.34 1.168 0.9605 0.4663668
+Income 18 24.73 1.374 1.1301 0.3192198
+Religion 4 25.11 6.279 5.1638 0.0004495 ***
+trust.in.science_7 1 99.73 99.728 82.0222 < 2.2e-16 ***
+need_for_cognition 1 1.99 1.989 1.6360 0.2015540
+interpersonal.trust_1 1 10.18 10.178 8.3706 0.0040023 **
+affect.science_1:affect.clarity_1 1 7.06 7.065 5.8105 0.0163391 *
+affect.science_1:affect.aesthetic_1 1 0.60 0.604 0.4971 0.4811667
+affect.clarity_1:affect.aesthetic_1 1 3.71 3.706 3.0481 0.0815296 .
+affect.science_1:affect.clarity_1:affect.aesthetic_1 1 0.30 0.304 0.2501 0.6172265
+Residuals 441 536.20 1.216
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+
+emmeans(aov(data.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)
+
+NOTE: Results may be misleading due to involvement in interactions
+
+
+ affect.clarity_1 emmean SE df lower.CL upper.CL
+ 75.7 5.17 0.0558 545 5.06 5.28
+
+Confidence level used: 0.95
+
@@ -2756,7 +3294,7 @@ Factor Analysis
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diff --git a/supplementary_materials/VisTrust_Survey.pdf b/supplementary_materials/VisTrust_Survey.pdf
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